# Inference of Deterministic Finite Automata via Q-Learning

arXiv: https://arxiv.org/abs/2510.17386 · HTML: https://mlsanity.com/paper/2510.17386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Interpretable and logic-guided RL, one of 15 directions in Deep reinforcement learning methods.
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Placed in

- Deep reinforcement learning methods / [Interpretable and logic-guided RL](https://mlsanity.com/deeprl/interpretable-and-logic-guided-rl/)

## Builds on (most cited first, 1 of 1)

- [A Robust Class of Data Languages and an Application to Learning](https://mlsanity.com/paper/1411.6646/) (2014)

## Abstract (arXiv)

Traditional approaches to inference of deterministic finite-state automata (DFA) stem from symbolic AI, including both active learning methods (e.g., Angluin's L* algorithm and its variants) and passive techniques (e.g., Biermann and Feldman's method, RPNI). Meanwhile, sub-symbolic AI, particularly machine learning, offers alternative paradigms for learning from data, such as supervised, unsupervised, and reinforcement learning (RL). This paper investigates the use of Q-learning, a well-known reinforcement learning algorithm, for the passive inference of deterministic finite automata. It builds on the core insight that the learned Q-function, which maps state-action pairs to rewards, can be reinterpreted as the transition function of a DFA over a finite domain. This provides a novel bridge between sub-symbolic learning and symbolic representations. The paper demonstrates how Q-learning can be adapted for automaton inference and provides an evaluation on several examples.